Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters

Abstract Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building...

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Autores principales:	Anar Amgalan, Patrick Taylor, Lilianne R. Mujica-Parodi, Hava T. Siegelmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4437a08b9cd74b978b311f5678a4bb4a
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spelling	oai:doaj.org-article:4437a08b9cd74b978b311f5678a4bb4a2021-12-02T15:54:06ZUnique scales preserve self-similar integrate-and-fire functionality of neuronal clusters10.1038/s41598-021-82461-42045-2322https://doaj.org/article/4437a08b9cd74b978b311f5678a4bb4a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82461-4https://doaj.org/toc/2045-2322Abstract Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (“integrate and fire”) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary “growth spurts” in brain size, as per punctuated equilibrium theories in evolutionary biology.Anar AmgalanPatrick TaylorLilianne R. Mujica-ParodiHava T. SiegelmannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anar Amgalan Patrick Taylor Lilianne R. Mujica-Parodi Hava T. Siegelmann Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
description	Abstract Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (“integrate and fire”) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary “growth spurts” in brain size, as per punctuated equilibrium theories in evolutionary biology.
format	article
author	Anar Amgalan Patrick Taylor Lilianne R. Mujica-Parodi Hava T. Siegelmann
author_facet	Anar Amgalan Patrick Taylor Lilianne R. Mujica-Parodi Hava T. Siegelmann
author_sort	Anar Amgalan
title	Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
title_short	Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
title_full	Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
title_fullStr	Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
title_full_unstemmed	Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
title_sort	unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4437a08b9cd74b978b311f5678a4bb4a
work_keys_str_mv	AT anaramgalan uniquescalespreserveselfsimilarintegrateandfirefunctionalityofneuronalclusters AT patricktaylor uniquescalespreserveselfsimilarintegrateandfirefunctionalityofneuronalclusters AT liliannermujicaparodi uniquescalespreserveselfsimilarintegrateandfirefunctionalityofneuronalclusters AT havatsiegelmann uniquescalespreserveselfsimilarintegrateandfirefunctionalityofneuronalclusters
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Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters

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